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Fitting Statistical Models to Data with Python

Fitting Statistical Models to Data with Python

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12 hours
English
University of Michigan
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For Rs. 5944
Intermediate
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Checklist

Certification

You will get a certificate on completing this course.

University

The course is from one of the top universities of the world - University of Michigan.

Price

This course is costly - Rs. 5944/-.

Difficulty

The students of this course have found this course difficult.

Content

The students of this course have liked the content of this course.

Assignments

The students of this course have not liked the assignments of this course.

Teaching

The students of this course have liked how the instructor has taught this course.

Satisfaction

The students of this course are overall satisfied with this course.

Edvicer's Rewards

You can get a cashback of ₹ 400 on buying this course.

Why should you choose this course?

Description

In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations. This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python). During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week's statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

Syllabus

WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING
WEEK 2 - FITTING MODELS TO INDEPENDENT DATA
WEEK 3 - FITTING MODELS TO DEPENDENT DATA
WEEK 4: Special Topics

What others say about this course

Reviews from Coursera

Great lecture content, poor quiz design. Hard to apply any of the concepts that you learn.

I was looking for an application course that would help with using Python with real world data. This was a theory course that added a small poorly explained notebook and a very brief lecture which did  Read More ...

If you don't already understand the topic don't bother with this course, the lectures are 95% hand waving and showing formulas they don't explain how to make sense of and then the quizzes are answerin  Read More ...

I think the content here is great and Mr. West is a wonderful teacher. That being said I do believe the multi-level regression model topics were quite difficult to understand and it did feel like some   Read More ...

The most impressive part is Week 2 Linear and Logistic Regression model fitting, Professor Brenda is Brilliant! She has the magic to explain complicated and abstract concept into a very easily understa  Read More ...

Show more reviews

FAQs

How to learn Python?

You can start with a basic Python course which covers everything in Python on a basic level like Complete Python Bootcamp: Go from zero to hero in Python 3 by Udemy. This can be followed by an advanced course in the fields you are interested in like Python Data Structures by Coursera for competitive programming, Applied Data Science with Python by Coursera for Machine Learning and Data Science.

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Python is one of the fastest-growing programming languages right now. It is especially used for data science and machine learning endeavors. At present, there are more than a few opportunities for Python developers. Besides, it is very easy to learn.

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How long does it take to learn Python?

Any programming language has two aspects; the syntax, and the library. While the former might require only a few days to learn, the latter is a life-long learning task. However, you can get started with Python professionally after 6 to 7 months of dedicated practice.

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What should I learn; Python or JavaScript?

Learning to choose among Python and JavaScript depends on the purpose you're learning them for. If you want to involve in data science and machine learning then Python is the ideal pick while JS is the go-to option when looking forward to web development. If you wish to simply learn one of them in order to get started with programming then Python might be the best bet. This is because it is beginner-friendly. JavaScript is not an easy programming language. Nonetheless, learning both gives better career advantages.

Can I learn Python without a programming background?

Yes, you can learn Python even without a programming background. However, it is surely a good thing if you first learn the basic programming terminology.

How long does it take to learn Django and Python?

Learning Python and Django are a never-ending process. However, to reach a level of being able to work with both Python and Django, at least 6 months are required.

Can I learn Python on my own?

Yes, definitely. You need to simply learn new Python concepts and then practice them to know better. Going through YouTube video lessons and the best Python tutorials might also help while self-learning Python.

What can I learn after learning Python?

After learning Python, the top skills you can learn to open highest number of job profiles for you are MySql, Data Analytics, and Web Analytics. The top skills that can get you jobs with highest salaries are Spring Boot,SAP, and Spring

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